Overview
What this challenge is about.
Define churn on edtech data, engineer user-week features, and train three models to beat a baseline. Finish with a notebook and memo for a verifiable certificate.
The scenario
The Lisbon edtech (around 80 staff, Series A) discounts heavily on annual renewals and wants to know which monthly users to target with a save offer before their card is charged again.
The Brief
What you'll do, and what you'll demonstrate.
Deliver a reproducible, honestly-evaluated churn-prediction mini-project that beats the recency baseline on a business-aligned metric.
Earning criteria — what you'll demonstrate
- Scope a real ML problem from a vague business ask
- Implement and evaluate multiple model families fairly
- Pick metrics aligned with the downstream business action
- Document an ML mini-project so non-ML teammates can rerun it
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Feature Engineering
Apply feature engineering to solve real industry problems and demonstrate production-level capability.
- Model Evaluation
Apply model evaluation to solve real industry problems and demonstrate production-level capability.
- Gradient Boosting
Apply gradient boosting to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Pytorch
Apply pytorch to solve real industry problems and demonstrate production-level capability.
- Data Cleaning
Apply data cleaning to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Machine Learning Engineer
Owning a churn project from problem framing to a reproducible pipeline that another teammate can rerun is the day-one work expected of a junior MLE on a small data team.
This challenge sharpens
- feature-engineering
- model-evaluation
- python
Data Scientist
Picking business-aligned metrics, calibrating probabilities, and writing the memo that explains what the model can and cannot do is the heart of applied data-scientist work.
This challenge sharpens
- model-evaluation
- data-cleaning
- feature-engineering
Applied AI Scientist
Comparing three model families on a real dataset and defending the winner in writing mirrors the applied AI scientist's job of mapping research methods onto product problems.
This challenge sharpens
- gradient-boosting
- pytorch
- model-evaluation